The Scaling of Performance and Losses in Miniature Internal Combustion Engines

Miniature glow ignition internal combustion (IC) piston engines are an off-the-shelf technology that could dramatically increase the endurance of miniature electric power supplies and the range and endurance of small unmanned air vehicles provided their overall thermodynamic efficiencies can be increased to 15% or better. This thesis presents the first comprehensive analysis of small (reliable measurements of engine performance and losses in these small engines. Methodologies are also developed for measuring volumetric, heat transfer, exhaust, mechanical, and combustion losses. These instruments and techniques are used to investigate the performance of seven single-cylinder, two-stroke, glow fueled engines ranging in size from 15 to 450 g (0.16 to 7.5 cm3 displacement). Scaling rules for power output, overall efficiency, and normalized power are developed from the data. These will be useful to developers of micro-air vehicles and miniature power systems. The data show that the minimum length scale of a thermodynamically viable piston engine based on present technology is approximately 3 mm. Incomplete combustion is the most important challenge as it accounts for 60-70% of total energy losses. Combustion losses are followed in order of importance by heat transfer, sensible enthalpy, and friction. A net heat release analysis based on in-cylinder pressure measurements suggest that a two-stage combustion process occurs at low engine speeds and equivalence ratios close to 1. Different theories based on burning mode and reaction kinetics are proposed to explain the observed results. High speed imaging of the combustion chamber suggests that a turbulent premixed flame with its origin in the vicinity of the glow plug is the primary driver of combustion. Placing miniature IC engines on a turbulent combustion regime diagram shows that they operate in the 'flamelet in eddy' regime whereas conventional-scale engines operate mostly in the 'wrinkled laminar flame sheet' regime. Taken together, the results show that the combustion process is the key obstacle to realizing the potential of small IC engines. Overcoming this obstacle will require new diagnostic techniques, measurements, combustion models, and high temperature materials

Automatic Detectors for Underwater Soundscape Measurements

Environmental impact regulations require that marine industrial operators quantify their contribution to underwater noise scenes. Automation of such assessments becomes feasible with the successful categorisation of sounds into broader classes based on source types – biological, anthropogenic and physical. Previous approaches to passive acoustic monitoring have mostly been limited to a few specific sources of interest. In this study, source-independent signal detectors are developed and a framework is presented for the automatic categorisation of underwater sounds into the aforementioned classes

Quasi-Positive Delta Sequences and Their Applications in Wavelet Approximation

A sufficient literature is available for the wavelet error of approximation of certain functions in the L2-norm. There is no work in context of multiresolution approximation of a function in the sense of sup-error. In this paper, for the first time, wavelet estimator for the approximation of a function f belonging to Lipα[a,b] class under supremum norm has been obtained. Working in this direction, four new theorems on the wavelet approximation of a function f belonging to Lipα,0<α≤1 class using the projection Pmf of its wavelet expansions have been estimated. The calculated estimator is best possible in wavelet analysis

On Cesàro's Means of First Order of Wavelet Packet Series

A novel theory on (C; 1); Cesàro's summability of order 1 of waveletpacket series is obtained in this study

Shear-strain-induced Spatially Varying Super-lattice Structures on Graphite studied by STM

We report on the Scanning Tunneling Microscope (STM) observation of linear fringes together with spatially varying super-lattice structures on (0001) graphite (HOPG) surface. The structure, present in a region of a layer bounded by two straight carbon fibers, varies from a hexagonal lattice of 6nm periodicity to nearly a square lattice of 13nm periodicity. It then changes into a one-dimensional (1-D) fringe-like pattern before relaxing into a pattern-free region. We attribute this surface structure to a shear strain giving rise to a spatially varying rotation of the affected graphite layer relative to the bulk substrate. We propose a simple method to understand these moire patterns by looking at the fixed and rotated lattices in the Fourier transformed k-space. Using this approach we can reproduce the spatially varying 2-D lattice as well as the 1-D fringes by simulation. The 1-D fringes are found to result from a particular spatial dependence of the rotation angle.Comment: 14 pages, 6 figure

SHGs as a media to involve youth in Grass root Development

The phenomenal transformation of the Ind ian economy from a begging bowl to a feeding basket can be resorted to the paradigm shift in the institutional and policy changes in the different economic and social parameters in the country. Nevertheless by and large the biggest problem in India has been the acute unemployment and related social indicators. The problem aSSumes greater significance to the nation which has youth as the major group in the populatio

Application of data engineering approaches to address challenges in microbiome data for optimal medical decision-making

The human gut microbiota is known to contribute to numerous physiological functions of the body and also implicated in a myriad of pathological conditions. Prolific research work in the past few decades have yielded valuable information regarding the relative taxonomic distribution of gut microbiota. Unfortunately, the microbiome data suffers from class imbalance and high dimensionality issues that must be addressed. In this study, we have implemented data engineering algorithms to address the above-mentioned issues inherent to microbiome data. Four standard machine learning classifiers (logistic regression (LR), support vector machines (SVM), random forests (RF), and extreme gradient boosting (XGB) decision trees) were implemented on a previously published dataset. The issue of class imbalance and high dimensionality of the data was addressed through synthetic minority oversampling technique (SMOTE) and principal component analysis (PCA). Our results indicate that ensemble classifiers (RF and XGB decision trees) exhibit superior classification accuracy in predicting the host phenotype. The application of PCA significantly reduced testing time while maintaining high classification accuracy. The highest classification accuracy was obtained at the levels of species for most classifiers. The prototype employed in the study addresses the issues inherent to microbiome datasets and could be highly beneficial for providing personalized medicine

Estimates for the nonlinear viscoelastic damped wave equation on compact Lie groups

Let $G$ be a compact Lie group. In this article, we investigate the Cauchy problem for a nonlinear wave equation with the viscoelastic damping on $G$. More preciously, we investigate some $L^2$-estimates for the solution to the homogeneous nonlinear viscoelastic damped wave equation on $G$ utilizing the group Fourier transform on $G$. We also prove that there is no improvement of any decay rate for the norm $\|u(t,\cdot)\|_{L^2(G)}$ by further assuming the $L^1(G)$-regularity of initial data. Finally, using the noncommutative Fourier analysis on compact Lie groups, we prove a local in time existence result in the energy space $\mathcal{C}^1([0,T],H^1_{\mathcal L}(G)).$Comment: 16 pages. arXiv admin note: text overlap with arXiv:2207.0442